import gradio as gr import pandas as pd import gc import torch import json from cognitive_mapping_probe.orchestrator_seismograph import run_seismic_analysis from cognitive_mapping_probe.auto_experiment import run_auto_suite, get_curated_experiments from cognitive_mapping_probe.prompts import RESONANCE_PROMPTS from cognitive_mapping_probe.utils import dbg theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(body_background_fill="#f0f4f9", block_background_fill="white") def cleanup_memory(): """Räumt Speicher nach jedem Experimentlauf auf.""" dbg("Cleaning up memory...") gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() dbg("Memory cleanup complete.") def run_single_analysis_display(*args, progress=gr.Progress(track_tqdm=True)): """Wrapper für den 'Manual Single Run'-Tab.""" # (Bleibt unverändert) pass # Platzhalter PLOT_PARAMS_DEFAULT = { "x": "Step", "y": "Value", "color": "Metric", "title": "Comparative Cognitive Dynamics", "color_legend_title": "Metrics", "color_legend_position": "bottom", "show_label": True, "height": 400, "interactive": True } def run_auto_suite_display(model_id, num_steps, seed, experiment_name, progress=gr.Progress(track_tqdm=True)): """Wrapper, der nun die speziellen Plots für ACT und Mechanistic Probe handhaben kann.""" summary_df, plot_df, all_results = run_auto_suite(model_id, int(num_steps), int(seed), experiment_name, progress) dataframe_component = gr.DataFrame(label="Comparative Statistical Signature", value=summary_df, wrap=True, row_count=(len(summary_df), "dynamic")) if experiment_name == "ACT Titration (Point of No Return)": plot_params_act = { "x": "Patch Step", "y": "Post-Patch Mean Delta", "title": "Attractor Capture Time (ACT) - Phase Transition", "mark": "line", "show_label": True, "height": 400, "interactive": True } new_plot = gr.LinePlot(value=plot_df, **plot_params_act) # --- NEU: Spezielle Plot-Logik für die mechanistische Sonde --- elif experiment_name == "Mechanistic Probe (Attention Entropies)": plot_params_mech = { "x": "Step", "y": "Value", "color": "Metric", "title": "Mechanistic Analysis: State Delta vs. Attention Entropy", "color_legend_title": "Metric", "show_label": True, "height": 400, "interactive": True } new_plot = gr.LinePlot(value=plot_df, **plot_params_mech) else: # Passe die Parameter an, um mit der geschmolzenen DataFrame-Struktur zu arbeiten plot_params_dynamic = PLOT_PARAMS_DEFAULT.copy() plot_params_dynamic['y'] = 'Delta' plot_params_dynamic['color'] = 'Experiment' new_plot = gr.LinePlot(value=plot_df, **plot_params_dynamic) serializable_results = json.dumps(all_results, indent=2, default=str) cleanup_memory() return dataframe_component, new_plot, serializable_results with gr.Blocks(theme=theme, title="Cognitive Seismograph 2.3") as demo: gr.Markdown("# 🧠 Cognitive Seismograph 2.3: Advanced Experiment Suite") with gr.Tabs(): with gr.TabItem("🔬 Manual Single Run"): gr.Markdown("Run a single experiment with manual parameters to explore specific hypotheses.") with gr.Row(variant='panel'): with gr.Column(scale=1): gr.Markdown("### 1. General Parameters") manual_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID") manual_prompt_type = gr.Radio(choices=list(RESONANCE_PROMPTS.keys()), value="resonance_prompt", label="Prompt Type") manual_seed = gr.Slider(1, 1000, 42, step=1, label="Seed") manual_num_steps = gr.Slider(50, 1000, 300, step=10, label="Number of Internal Steps") gr.Markdown("### 2. Modulation Parameters") manual_concept = gr.Textbox(label="Concept to Inject", placeholder="e.g., 'calmness'") manual_strength = gr.Slider(0.0, 5.0, 1.5, step=0.1, label="Injection Strength") manual_run_btn = gr.Button("Run Single Analysis", variant="primary") with gr.Column(scale=2): gr.Markdown("### Single Run Results") manual_verdict = gr.Markdown("Analysis results will appear here.") manual_plot = gr.LinePlot(x="Internal Step", y="State Change (Delta)", title="Internal State Dynamics", show_label=True, height=400) with gr.Accordion("Raw JSON Output", open=False): manual_raw_json = gr.JSON() manual_run_btn.click( fn=run_single_analysis_display, inputs=[manual_model_id, manual_prompt_type, manual_seed, manual_num_steps, manual_concept, manual_strength], outputs=[manual_verdict, manual_plot, manual_raw_json] ) with gr.TabItem("🚀 Automated Suite"): gr.Markdown("Run a predefined, curated suite of experiments and visualize the results comparatively.") with gr.Row(variant='panel'): with gr.Column(scale=1): gr.Markdown("### Auto-Experiment Parameters") auto_model_id = gr.Textbox(value="google/gemma-3-4b-it", label="Model ID") auto_num_steps = gr.Slider(50, 1000, 300, step=10, label="Steps per Run") auto_seed = gr.Slider(1, 1000, 42, step=1, label="Seed") auto_experiment_name = gr.Dropdown( choices=list(get_curated_experiments().keys()), # Setze das neue mechanistische Experiment als Standard value="Mechanistic Probe (Attention Entropies)", label="Curated Experiment Protocol" ) auto_run_btn = gr.Button("Run Curated Auto-Experiment", variant="primary") with gr.Column(scale=2): gr.Markdown("### Suite Results Summary") auto_plot_output = gr.LinePlot(**PLOT_PARAMS_DEFAULT) auto_summary_df = gr.DataFrame(label="Comparative Statistical Signature", wrap=True) with gr.Accordion("Raw JSON for all runs", open=False): auto_raw_json = gr.JSON() auto_run_btn.click( fn=run_auto_suite_display, inputs=[auto_model_id, auto_num_steps, auto_seed, auto_experiment_name], outputs=[auto_summary_df, auto_plot_output, auto_raw_json] ) if __name__ == "__main__": # (launch() wird durch Gradio's __main__-Block aufgerufen) demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)